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owl-map
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Show match candidates

This commit is contained in:
Edward Betts 2021-05-13 00:11:52 +02:00
parent 5b13ca1595
commit ad60f9f561
3 changed files with 457 additions and 45 deletions
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97
static/js/map.js
View File

@ -21,13 +21,20 @@ var loading = document.getElementById("loading");
var load_text = document.getElementById("load-text");
var isa_card = document.getElementById("isa-card");
var detail_card = document.getElementById("detail-card");
var detail = document.getElementById("detail");
var detail_image = document.getElementById("detail-image");
var detail_header = document.getElementById("detail-header");
var detail_qid;
var candidates = document.getElementById("candidates");
var checkbox_list = document.getElementsByClassName("isa-checkbox");
var nearby_lookup = {};
var isa_labels = {};
var items_url = "/api/1/items";
var osm_objects_url = "/api/1/osm";
var missing_url = "/api/1/missing";
var hover_circles = [];
var selected_circles = [];
var candidate_outline;
var isa_count = {};
@ -267,7 +274,7 @@ function build_item_detail(item, tag_or_key_list) {
var popup = "<p><strong>Wikidata item</strong><br>";
popup += `<a href="${wd_url}" target="_blank">${item.label}</a> (${item.qid})`;
if (item.description) {
popup += `<br>description: ${item.description}`;
popup += `<br><strong>description</strong><br>${item.description}`;
}
if (item.isa_list && item.isa_list.length) {
popup += "<br><strong>item type</strong>";
@ -279,17 +286,14 @@ function build_item_detail(item, tag_or_key_list) {
}
if (tag_or_key_list && tag_or_key_list.length) {
popup += "<br><strong>OSM tags/keys to search for</strong>"
popup += "<br><strong>OSM tags/keys to search for</strong>";
for (const v of tag_or_key_list) {
popup += `<br>${v}`;
}
}
if (item.image_list && item.image_list.length) {
popup += `<br><img class="w-100" src="/commons/${item.image_list[0]}">`;
}
if (item.street_address && item.street_address.length) {
popup += "<br><strong>street address</strong>"
popup += "<br><strong>street address</strong>";
popup += `<br>${item.street_address[0]["text"]}`;
}
@ -310,7 +314,7 @@ function mouseover(item) {
item.markers.forEach((marker) => {
var coords = marker.getLatLng();
var circle = L.circle(coords, {radius: 25}).addTo(map);
var circle = L.circle(coords, { radius: 20 }).addTo(map);
hover_circles.push(circle);
});
}
@ -336,6 +340,19 @@ function close_item_details() {
circle.removeFrom(map);
});
selected_circles = [];
detail_header.innerHTML = "";
detail.innerHTML = "";
candidates.innerHTML = "";
detail_qid = undefined;
detail_image.setAttribute("src", "");
detail_image.classList.add("d-none");
if (candidate_outline) {
candidate_outline.removeFrom(map);
candidate_outline = undefined;
}
}
function mouse_events(marker, qid) {
@ -348,6 +365,7 @@ function mouse_events(marker, qid) {
mouseout(item);
});
marker.on("click", function () {
var wd_item = items[qid].wikidata;
isa_card.classList.add("visually-hidden");
detail_card.classList.remove("visually-hidden");
detail_card.classList.add("bg-highlight");
@ -355,24 +373,77 @@ function mouse_events(marker, qid) {
item.markers.forEach((marker) => {
var coords = marker.getLatLng();
var circle = L.circle(coords, {radius: 25, color: "orange"}).addTo(map);
var circle = L.circle(coords, { radius: 20, color: "orange" }).addTo(map);
selected_circles.push(circle);
});
window.setTimeout(function() {
window.setTimeout(function () {
detail_card.classList.remove("bg-highlight");
}, 500);
detail_qid = qid;
if (wd_item.image_list && wd_item.image_list.length) {
detail_image.setAttribute("src", `/commons/${wd_item.image_list[0]}`);
detail_image.classList.remove("d-none");
} else {
detail_image.setAttribute("src", "");
detail_image.classList.add("d-none");
}
var item_label = `${wd_item.label} (${wd_item.qid})`;
detail_header.innerHTML = "";
detail_header.append(document.createTextNode(item_label));
var item_tags_url = `/api/1/item/${qid}/tags`;
axios.get(item_tags_url).then((response) => {
var tag_or_key_list = response.data.tag_or_key_list;
var item_detail = build_item_detail(items[qid].wikidata, tag_or_key_list);
if (response.data.qid != detail_qid) {
tag_or_key_list = []; // different QID
}
var item_detail = build_item_detail(wd_item, tag_or_key_list);
detail.innerHTML = item_detail;
if (tag_or_key_list.length == 0) return;
var item_osm_candidates_url = `/api/1/item/${qid}/candidates`;
axios.get(item_osm_candidates_url).then((response) => {
console.log(response.data);
});
var bounds = map.getBounds();
var params = { bounds: bounds.toBBoxString() };
axios
.get(item_osm_candidates_url, { params: params })
.then((response) => {
if (response.data.qid != detail_qid) return; // different QID
var nearby = response.data.nearby;
if (!nearby.length) return;
var osm_html = "<strong>Possible OSM matches</strong><br>";
for (const osm of nearby) {
var span_id = osm.identifier.replace("/", "_");
nearby_lookup[span_id] = osm;
osm_html += `<span class="osm-candidate" id="${span_id}"> ${osm.distance.toFixed(
1
)}m ${osm.name || "no name"}</span><br>`;
}
candidates.innerHTML = osm_html;
var span_list = document.getElementsByClassName("osm-candidate");
for (const osm_span of span_list) {
osm_span.onmouseover = function (e) {
osm = nearby_lookup[e.target.id];
if (candidate_outline !== undefined) {
candidate_outline.removeFrom(map);
}
var mapStyle = { fillOpacity: 0, color: "red" };
var geojson = L.geoJSON(null, { style: mapStyle });
geojson.addData(osm.geojson);
geojson.addTo(map);
candidate_outline = geojson;
};
}
});
});
});

14
templates/map.html
View File

@ -47,12 +47,20 @@
<div class="card visually-hidden m-2" id="detail-card">
<div class="card-body">
<div class="h5 card-title">item detail
<button type="button" class="btn-close" id="close-detail"></button>
<div class="h4 card-title">
<span id="detail-header">item detail</span>
<button type="button" class="btn-close float-end" id="close-detail"></button>
</div>
<div id="detail"></div>
<div class="row">
<div class="col">
<div id="detail"></div>
</div>
<div class="col">
<div id="candidates"></div>
</div>
</div>
</div>
<img src="" class="card-img-bottom d-none" id="detail-image">
</div>
</div>

391
web_view.py
View File

@ -327,6 +327,7 @@ def get_and_save_item(qid):
raise
item.locations = model.location_objects(coords)
database.session.add(item)
database.session.commit()
return item
@ -375,23 +376,334 @@ def api_osm_objects():
return jsonify(success=True, objects=objects, duration=t1)
skip_isa = {13226383, 16686448, 2221906}
edu = ['Tag:amenity=college', 'Tag:amenity=university', 'Tag:amenity=school',
'Tag:office=educational_institution']
tall = ['Key:height', 'Key:building:levels']
extra_keys = {
'Q3914': ['Tag:building=school',
'Tag:building=college',
'Tag:amenity=college',
'Tag:office=educational_institution'], # school
'Q322563': edu, # vocational school
'Q383092': edu, # film school
'Q1021290': edu, # music school
'Q1244442': edu, # school building
'Q1469420': edu, # adult education centre
'Q2143781': edu, # drama school
'Q2385804': edu, # educational institution
'Q5167149': edu, # cooking school
'Q7894959': edu, # University Technical College
'Q47530379': edu, # agricultural college
'Q11303': tall, # skyscraper
'Q18142': tall, # high-rise building
'Q33673393': tall, # multi-storey building
'Q641226': ['Tag:leisure=stadium'], # arena
'Q2301048': ['Tag:aeroway=helipad'], # special airfield
'Q622425': ['Tag:amenity=pub',
'Tag:amenity=music_venue'], # nightclub
'Q187456': ['Tag:amenity=pub',
'Tag:amenity=nightclub'], # bar
'Q16917': ['Tag:amenity=clinic',
'Tag:building=clinic'], # hospital
'Q330284': ['Tag:amenity=market'], # marketplace
'Q5307737': ['Tag:amenity=pub',
'Tag:amenity=bar'], # drinking establishment
'Q875157': ['Tag:tourism=resort'], # resort
'Q174782': ['Tag:leisure=park',
'Tag:highway=pedestrian',
'Tag:foot=yes',
'Tag:area=yes',
'Tag:amenity=market',
'Tag:leisure=common'], # square
'Q34627': ['Tag:religion=jewish'], # synagogue
'Q16970': ['Tag:religion=christian'], # church
'Q32815': ['Tag:religion=islam'], # mosque
'Q811979': ['Key:building'], # architectural structure
'Q11691': ['Key:building'], # stock exchange
'Q1329623': ['Tag:amenity=arts_centre', # cultural centre
'Tag:amenity=community_centre'],
'Q856584': ['Tag:amenity=library'], # library building
'Q11315': ['Tag:landuse=retail'], # shopping mall
'Q39658032': ['Tag:landuse=retail'], # open air shopping centre
'Q277760': ['Tag:historic=folly',
'Tag:historic=city_gate'], # gatehouse
'Q180174': ['Tag:historic=folly'], # folly
'Q15243209': ['Tag:leisure=park',
'Tag:boundary=national_park'], # historic district
'Q3010369': ['Tag:historic=monument'], # opening ceremony
'Q123705': ['Tag:place=suburb'], # neighbourhood
'Q256020': ['Tag:amenity=pub'], # inn
'Q41253': ['Tag:amenity=theatre'], # movie theater
'Q17350442': ['Tag:amenity=theatre'], # venue
'Q156362': ['Tag:amenity=winery'], # winery
'Q14092': ['Tag:leisure=fitness_centre',
'Tag:leisure=sports_centre'], # gymnasium
'Q27686': ['Tag:tourism=hostel', # hotel
'Tag:tourism=guest_house',
'Tag:building=hotel'],
'Q11707': ['Tag:amenity=cafe', 'Tag:amenity=fast_food',
'Tag:shop=deli', 'Tag:shop=bakery',
'Key:cuisine'], # restaurant
'Q2360219': ['Tag:amenity=embassy'], # permanent mission
'Q27995042': ['Tag:protection_title=Wilderness Area'], # wilderness area
'Q838948': ['Tag:historic=memorial',
'Tag:historic=monument'], # work of art
'Q23413': ['Tag:place=locality'], # castle
'Q28045079': ['Tag:historic=archaeological_site',
'Tag:site_type=fortification',
'Tag:embankment=yes'], # contour fort
'Q744099': ['Tag:historic=archaeological_site',
'Tag:site_type=fortification',
'Tag:embankment=yes'], # hillfort
'Q515': ['Tag:border_type=city'], # city
'Q1254933': ['Tag:amenity=university'], # astronomical observatory
'Q1976594': ['Tag:landuse=industrial'], # science park
'Q190928': ['Tag:landuse=industrial'], # shipyard
'Q4663385': ['Tag:historic=train_station', # former railway station
'Tag:railway=historic_station'],
'Q11997323': ['Tag:emergency=lifeboat_station'], # lifeboat station
'Q16884952': ['Tag:castle_type=stately',
'Tag:building=country_house'], # country house
'Q1343246': ['Tag:castle_type=stately',
'Tag:building=country_house'], # English country house
'Q4919932': ['Tag:castle_type=stately'], # stately home
'Q1763828': ['Tag:amenity=community_centre'], # multi-purpose hall
'Q3469910': ['Tag:amenity=community_centre'], # performing arts center
'Q57660343': ['Tag:amenity=community_centre'], # performing arts building
'Q163740': ['Tag:amenity=community_centre', # nonprofit organization
'Tag:amenity=social_facility',
'Key:social_facility'],
'Q41176': ['Key:building:levels'], # building
'Q44494': ['Tag:historic=mill'], # mill
'Q56822897': ['Tag:historic=mill'], # mill building
'Q2175765': ['Tag:public_transport=stop_area'], # tram stop
'Q179700': ['Tag:memorial=statue', # statue
'Tag:memorial:type=statue',
'Tag:historic=memorial'],
'Q1076486': ['Tag:landuse=recreation_ground'], # sports venue
'Q988108': ['Tag:amenity=community_centre', # club
'Tag:community_centre=club_home'],
'Q55004558': ['Tag:service=yard',
'Tag:landuse=railway'], # car barn
'Q19563580': ['Tag:landuse=railway'], # rail yard
'Q134447': ['Tag:generator:source=nuclear'], # nuclear power plant
'Q1258086': ['Tag:leisure=park',
'Tag:boundary=national_park'], # National Historic Site
'Q32350958': ['Tag:leisure=bingo'], # Bingo hall
'Q53060': ['Tag:historic=gate', # gate
'Tag:tourism=attraction'],
'Q3947': ['Tag:tourism=hotel', # house
'Tag:building=hotel',
'Tag:tourism=guest_house'],
'Q847017': ['Tag:leisure=sports_centre'], # sports club
'Q820477': ['Tag:landuse=quarry',
'Tag:gnis:feature_type=Mine'], # mine
'Q77115': ['Tag:leisure=sports_centre'], # community center
'Q35535': ['Tag:amenity=police'], # police
'Q16560': ['Tag:tourism=attraction', # palace
'Tag:historic=yes'],
'Q131734': ['Tag:amenity=pub', # brewery
'Tag:industrial=brewery'],
'Q828909': ['Tag:landuse=commercial',
'Tag:landuse=industrial',
'Tag:historic=dockyard'], # wharf
'Q10283556': ['Tag:landuse=railway'], # motive power depot
'Q18674739': ['Tag:leisure=stadium'], # event venue
'Q20672229': ['Tag:historic=archaeological_site'], # friary
'Q207694': ['Tag:museum=art'], # art museum
'Q22698': ['Tag:leisure=dog_park',
'Tag:amenity=market',
'Tag:place=square',
'Tag:leisure=common'], # park
'Q738570': ['Tag:place=suburb'], # central business district
'Q1133961': ['Tag:place=suburb'], # commercial district
'Q935277': ['Tag:gnis:ftype=Playa',
'Tag:natural=sand'], # salt pan
'Q14253637': ['Tag:gnis:ftype=Playa',
'Tag:natural=sand'], # dry lake
'Q63099748': ['Tag:tourism=hotel', # hotel building
'Tag:building=hotel',
'Tag:tourism=guest_house'],
'Q2997369': ['Tag:leisure=park',
'Tag:highway=pedestrian',
'Tag:foot=yes',
'Tag:area=yes',
'Tag:amenity=market',
'Tag:leisure=common'], # plaza
'Q130003': ['Tag:landuse=winter_sports', # ski resort
'Tag:site=piste',
'Tag:leisure=resort',
'Tag:landuse=recreation_ground'],
'Q4830453': ['Key:office',
'Tag:building=office'], # business
}
skip_isa = {
13226383,
16686448,
2221906,
2133296, # space (architecture)
56052926, # building division
15989253, # part
9350592, # telecommunications infrastructure
121359, # infrastructure
28877, # goods
2897903, # goods and services
2995644, # result
733541, # consequence
408386, # inference
3249551, # process
20937557, # series
16887380, # group
28813620, # set
99527517, # collection entity
1150070, # change
1190554, # occurrence
26907166, # temporal entity
2425052, # electrical appliance
931447, # electrical load
210729, # electrical element
3749263, # electrical device
16798631, # equipment
66310125, # nonbiological component
22811462, # type of manufactured good
21146257, # type
16889133, # class
1310239, # component
337060, # perceptible object
581105, # consumer electronics
2858615, # electronic machine
1183543, # device
39546, # tool
35825432, # converter
11019, # machine
8205328, # artificial physical object
223557, # physical object
35459920, # three-dimensional object
488383, # object
35120, # entity
1454986, # physical system
30060700, # scientific object
58778, # system
6671777, # structure
4406616, # concrete object
2555640, # cell (architecture)
78642244, # closed space
1902617, # verblijfsruimte
180516, # room ['Key:room']
17334923, # location
27096213, # geographic entity
58416391, # spatial entity
58415929, # spatio-temporal entity
811979, # architectural structure
811430, # human-made geographic feature
35145743, # human-made landform
27096235, # artificial geographic entity
618123, # geographical feature
386724, # work
15401930, # product
102074988, # artificial physical structure
15710813, # physical structure
1299240, # interior space
4830453, # business
3563237, # economic unit
2198779, # unit
7184903, # abstract object
43229, # organization
16334295, # group of humans
16334298, # group of living things
61961344, # group of physical objects
98119401, # group or class of physical objects
106559804, # person or organization
24229398, # agent
23958946, # individual entity
4830453, # business
2695280, # technique
21162272, # means
4026292, # action
1914636, # activity
372222, # human-readable medium
494756, # data
42848, # data
1166770, # depiction
11024, # communication
6031064, # information exchange
52948, # interaction
23009552, # exchange
23009675, # transfer
22294683, # biological process involved in intraspecies interaction between organisms
628858, # workplace
1228250, # line
211548, # locus
36161, # set
864377, # multiset
246672, # mathematical object
5469988, # formalization
4393498, # representation
930933, # relation
217594, # class
294440, # public space
7551384, # social space
83493482, # thanking
83492918, # acknowledgement
628523, # message
11028, # information
189970, # social status
11424100, # status
4897819, # role
1207505, # quality
937228, # property
11862829, # academic discipline
1047113, # specialty
9081, # knowledge
104127086, # memory
12488383, # content
2434238, # heritage
23893363, # heritage
82821, # tradition
251777, # custom
1299714, # habit
36529775, # habit
7302601, # recognition
}
skip_tags = {"Key:addr:street"}
def get_items(item_ids):
items = []
for item_id in item_ids:
item = model.Item.query.get(item_id)
if not item:
print(f"get Q{item_id}")
if not get_and_save_item(f"Q{item_id}"):
continue
item = model.Item.query.get(item_id)
items.append(item)
return items
def get_item_tags(item):
isa_items = []
isa_list = [v["numeric-id"] for v in item.get_claim("P31")]
isa_items = model.Item.query.filter(model.Item.item_id.in_(isa_list)).all()
isa_items = get_items(isa_list)
osm_list = set()
seen = set(isa_list) | skip_isa
while isa_items:
isa = isa_items.pop()
if not isa:
continue
osm = [v for v in isa.get_claim("P1282") if v not in skip_tags]
if isa.qid in extra_keys:
osm += extra_keys[isa.qid]
print(isa.qid, isa.label(), osm)
osm_list.update(osm)
subclass_of = [v["numeric-id"] for v in isa.get_claim("P279")]
subclass_of = [v["numeric-id"] for v in (isa.get_claim("P279") or []) if v]
isa_list = [isa_id for isa_id in subclass_of if isa_id not in seen]
seen.update(isa_list)
isa_items += model.Item.query.filter(model.Item.item_id.in_(isa_list)).all()
isa_items += get_items(isa_list)
return sorted(osm_list)
@ -404,59 +716,80 @@ def api_get_item_tags(item_id):
return jsonify(success=True, qid=item.qid, tag_or_key_list=osm_list, duration=t1)
def get_tag_filter(item):
osm_list = get_item_tags(item)
def get_tag_filter(cls, tag_list):
tag_filter = []
for tag_or_key in osm_list:
for tag_or_key in tag_list:
if tag_or_key.startswith("Key:"):
tag_filter.append(model.Polygon.tags.has_key(tag_or_key[4:]))
tag_filter.append(cls.tags.has_key(tag_or_key[4:]))
if tag_or_key.startswith("Tag:"):
k, _, v = tag_or_key.partition("=")
tag_filter.append(model.Polygon.tags[k] == v)
tag_filter.append(cls.tags[k[4:]] == v)
return or_(*tag_filter)
return tag_filter
def get_nearby(bbox, item, max_distance=200):
db_bbox = make_envelope(bbox)
def get_nearby(item, max_distance=100):
osm_objects = {}
distances = {}
tag_filter = get_tag_filter(item)
tag_list = get_item_tags(item)
if not tag_list:
return []
for loc in item.locations:
lat, lon = loc.get_lat_lon()
point = func.ST_SetSRID(func.ST_MakePoint(lon, lat), 4326)
for cls in model.Point, model.Line, model.Polygon:
tag_filter = get_tag_filter(cls, tag_list)
dist = func.ST_Distance(point, cls.way.cast(Geography(srid=4326)))
dist = func.ST_Distance(point, model.Polygon.way.cast(Geography()))
q = (model.Polygon.query
.add_columns(dist.label('distance'))
.filter(dist < max_distance, tag_filter)
.order_by(point.distance_centroid(model.Polygon.way))
q = (cls.query.add_columns(dist.label('distance'))
.filter(
func.ST_Intersects(db_bbox, cls.way),
func.ST_Area(cls.way) < 20 * func.ST_Area(db_bbox),
or_(*tag_filter))
.order_by(point.distance_centroid(cls.way))
.limit(20))
for i, dist in q:
osm_objects.setdefault(i.identifier, i)
if i.identifier not in distances or dist < distances[i.identifier]:
distances[i.identifier] = dist
# print(q.statement.compile(compile_kwargs={"literal_binds": True}))
return [(osm_objects[identifier], dist)
for identifier, dist
in sorted(distances.items(), key=lambda i:i[1])]
for i, dist in q:
if dist > max_distance:
continue
osm_objects.setdefault(i.identifier, i)
if i.identifier not in distances or dist < distances[i.identifier]:
distances[i.identifier] = dist
nearby = [(osm_objects[identifier], dist)
for identifier, dist
in sorted(distances.items(), key=lambda i:i[1])]
return nearby[:10]
@app.route("/api/1/item/Q<int:item_id>/candidates")
def api_find_osm_candidates(item_id):
bounds = request.args.get("bounds")
t0 = time()
item = model.Item.query.get(item_id)
max_distance = 100
nearby = []
for osm, dist in get_nearby(item, max_distance):
for osm, dist in get_nearby(bounds, item):
tags = osm.tags
name = osm.name or tags.get("addr:housename")
if not name and "addr:housenumber" in tags and "addr:street" in tags:
name = tags["addr:housenumber"] + " " + tags["addr:street"]
cur = {
"identifier": osm.identifier,
"distance": dist,
"tags": osm.tags,
"area": osm.area,
"name": name,
"tags": tags,
"geojson": osm.geojson(),
}
if hasattr(osm, 'area'):
cur["area"] = osm.area
nearby.append(cur)
t1 = time() - t0